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dc.contributor.authorZhang, Zhaoyang
dc.contributor.authorWang, Honggang
dc.contributor.authorWang, Chonggang
dc.contributor.authorFang, Hua (Julia)
dc.date2022-08-11T08:10:34.000
dc.date.accessioned2022-08-23T17:13:11Z
dc.date.available2022-08-23T17:13:11Z
dc.date.issued2015-02-09
dc.date.submitted2014-04-07
dc.identifier.citationZhang Z, Wang H, Wang C, Fang H. Cluster-based Epidemic Control Through Smartphone-based Body Area Networks. IEEE Trans Parallel Distrib Syst. 2015 Feb 9;26(3):681-690. PubMed PMID: 25741173; PubMed Central PMCID: PMC4346229. doi:10.1109/TPDS.2014.2313331. <a href="http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2313331" target="_blank">Link to article on publisher's site</a>
dc.identifier.doi10.1109/TPDS.2014.2313331
dc.identifier.pmid25741173
dc.identifier.urihttp://hdl.handle.net/20.500.14038/46668
dc.description.abstractIncreasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.
dc.language.isoen_US
dc.publisherIEEE
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25741173&dopt=Abstract">Link to article in PubMed</a>
dc.rights<p>Copyright 2014 IEEE. Accepted manuscript posted as allowed by the publisher's author rights policy at http://www.ieee.org/publications_standards/publications/rights/rights_policies.html.</p>
dc.subjectUMCCTS funding
dc.subjectDigital Communications and Networking
dc.subjectEpidemiology
dc.subjectHealth Information Technology
dc.subjectPublic Health
dc.subjectSystems and Communications
dc.subjectTheory and Algorithms
dc.titleCluster-based Epidemic Control Through Smartphone-based Body Area Networks
dc.typeAccepted Manuscript
dc.source.journaltitleIEEE Transactions on Parallel and Distributed Systems
dc.source.volume26
dc.source.issue3
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2127&amp;context=qhs_pp&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/1127
dc.identifier.contextkey5449060
refterms.dateFOA2022-08-23T17:13:11Z
html.description.abstract<p>Increasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.</p>
dc.identifier.submissionpathqhs_pp/1127
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages681-690


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